摘要
目的:比较几种模式识别方法在中药挥发油红外光谱法鉴别中的分类效果。方法:对多种忍冬属和柑橘属中药的挥发油进行红外光谱测定,应用系统聚类、K-均值聚类、人工神经网络、支持向量机方法对样品红外光谱进行分类。结果:系统聚类与K-均值聚类分类效果不佳,人工神经网络和支持向量机方法均取得100%分类正确率。结论:可以将人工神经网络和支持向量机模式识别方法与红外光谱法结合,构建化学计量学指纹图谱技术,用于中药挥发油的鉴别。
OBJECTIVE：To compare the performance of several pattern recognition methods in the identification of volatile oils of traditional Chinese medicine（TCM）by infrared spectroscopy. METHODS：The volatile oils of several Lonicera and Citrus TCM were determined by infrared spectroscopy. All samples of infrared spectrum were classified by hierarchical clustering,K-mean clustering,artificial neural networks,and support vector machine. RESULTS：The results of hierarchical clustering and K-mean clustering were ineffective. Methods of artificial neural networks and support vector machine achieved correct classification rate of100%. CONCLUSIONS：Artificial neural networks and support vector machine can be combined with infrared spectroscopy to create chemometric fingerprinting for the identification of volatile oils of TCM.
出处
《中国药房》
CAS
北大核心
2015年第21期2986-2988,共3页
China Pharmacy
基金
广东省中医药局科研课题(No.20122071)
关键词
中药
挥发油
红外光谱法
模式识别
化学计量学指纹图谱技术
Traditional Chinese medicine
Volatile oils
Infrared spectroscopy
Pattern recognition
Chemometric fingerprinting
